Yimin Yang

About Me

I am currently an Assistant Professor in the Department of Computer Science at the Lakehead University, Ontario, Canada. I am also a Faculty Affiliate at the Vector Institute, Toronto, Canada. My research interests are in Machine Learning, Neural Networks and Signal Processing. From 2014-2018, I was a Postdoctoral Fellow in the Centre of Computer Vision and Deep Learning at the University of Windsor, Ontario, Canada. From 2009-2013, I was a Ph.D. Candidate at the College of Electrical and Information Engineering, Hunan University, China, where I received my Ph.D degree with a specialization in Pattern Recognition and Intelligent System.
"The Fear of the LORD is the beginning of Wisdom, and knowledge of the Holy One is understanding. Proverbs 9:10"

Personal Information

Full Name
Yimin Yang
ADDRESS
955 Oliver Rd, Thunder Bay, Ontario, Canada P7B 5E1
e-mail
yyang48@lakeheadu.ca
Institution
Computer Science Department, Lakehead University

Research Interests

Machine Learning:

Artificial Neural Networks Methodology

Deep Learning

Unsupervised Learning

Ensemble Learning

Data Processing and Robotics:

Object Category and Image Recognition

Scene Understanding

Robot Perception and Navigation

Brain Signal Processing and Recognition

Selected Papers

Artificial Neural Networks Methodology:

(1) Yimin Yang, Q.M.Jonathan Wu and et al. Non-iterative recomputation of the dense layers for performance improvement of DCNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, In press. [link]
(2) Yimin Yang, Q.M.Jonathan Wu. Feature combined from hundreds of midlayers: Hierarchical networks with Subnetwork Nodes. IEEE Transactions on Neural Networks and Learning Systems, 2019, In press. [link]
(3) Yimin Yang, Q.M.Jonathan Wu. Multilayer extreme learning machine with subnetwork nodes for representation learning. IEEE Transactions on Cybernetics. 46 (11), 2570-2583, 2016. [link]
(4) Yimin Yang, Wang Yaonan, et al. Bidirectional extreme learning machine for regression problem and its learning effectiveness. IEEE Transactions on Neural Networks and Learning Systems. 23(9), 1498 – 1505. 2012. [link]
(5) Yimin Yang, Q.M.Jonathan Wu and et al. Data partition learning with multiple extreme learning machines. IEEE Transactions on Cybernetics. 45(8), 1463-1475. 2015. [link]
(6) Yimin Yang, Q.M.Jonathan Wu. Autoencoder with invertible functions for dimension reduction and image reconstruction. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 48(7), 1065-1079. [link]
(7) Yimin Yang, Q.M.Jonathan Wu. Extreme learning machine with subnetwork hidden nodes for regression and classification. IEEE Transactions on Cybernetics. 46 (12), 2885-2898, 2016. [link]
(8) Yimin Yang, Yaonan Wang, Q.M. Jonathan Wu and et al. Progressive learning method for general hybrid system approximation. IEEE Transactions on Neural Networks and Learning Systems. 26(9), 1855-1874, 2015. [link]
 

Data Processing and Robotics

(1) Yimin Yang, Q.M.Jonathan Wu, Wei-long Zheng, Bao-liang, Lu. EEG-based emotion recognition using hierarchical network with subnetwork nodes. IEEE Transactions on cognitive and developmental systems. 10(2), 408-419, 2018. [link]
(2) Yimin Yang, Yaonan Wang, et al. Hybrid Chaos optimization algorithm with artificial emotion. Applied mathematics and Computation. Vol. 218, pp. 6585-6611, 2012. [link]
(3) Yaonan Wang, Yongpeng Shen, Xiaofang Yuan, Yimin Yang. “Operating point optimization of auxiliary power unit based on dynamic combined cost map and particle swarm optimization.” IEEE Transactions on Power Electronics. 30 (12), 7038-7050, 2015. [link]
(4) Yimin Yang, Yaonan Wang, Xiaofang Yuan. Parallel chaos search based extreme learning machine. Neural Processing letters. Vol.37 pp. 277-301, 2013. [link]
(5) Yimin Yang, Yaonan Wang, Xiaofang Yuan, et al. Neural network based self-learning control for power transmission line deicing robot. Neural Computing and Applications. Vol. 22, pp. 969-986, 2013. [link]